import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				None





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_=0.9_9_9							,					UpperCamelCase_="cosine"							,					)		->	Tuple:







 '''simple docstring'''
 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCamelCase_       ):
   return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2       ) ** 2

 elif alpha_transform_type == "exp":

  def alpha_bar_fn(UpperCamelCase_       ):
   return math.exp(t * -1_2.0       )

 else:
  raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""       )

 UpperCamelCase			  =      []
 for i in range(UpperCamelCase_       ):
  UpperCamelCase			  =      i / num_diffusion_timesteps
  UpperCamelCase			  =      (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(UpperCamelCase_       ) / alpha_bar_fn(UpperCamelCase_       )							,					UpperCamelCase_       )       )
 return torch.tensor(UpperCamelCase_							,					dtype=torch.floataa       )


class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @register_to_config
   def __init__(     self						:						str   ,   lowerCamelCase_						:						int = 1000   ,   lowerCamelCase_						:						str = "fixed_small_log"   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						Optional[float] = 1.0   ,   lowerCamelCase_						:						str = "epsilon"   ,   lowerCamelCase_						:						str = "squaredcos_cap_v2"   ,   ):





    """simple docstring"""


    if beta_schedule != "squaredcos_cap_v2":
     raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""				)

    UpperCamelCase			  =      betas_for_alpha_bar(lowerCamelCase_				)

    UpperCamelCase			  =      1.0 - self.betas
    UpperCamelCase			  =      torch.cumprod(self.alphas   ,   dim=0				)
    UpperCamelCase			  =      torch.tensor(1.0				)

    # standard deviation of the initial noise distribution
    UpperCamelCase			  =      1.0

    # setable values
    UpperCamelCase			  =      None
    UpperCamelCase			  =      torch.from_numpy(np.arange(0   ,   lowerCamelCase_				)[::-1].copy()				)

    UpperCamelCase			  =      variance_type







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						Optional[int] = None				):





    """simple docstring"""


    return sample







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Union[str, torch.device] = None				):





    """simple docstring"""


    UpperCamelCase			  =      num_inference_steps
    UpperCamelCase			  =      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
    UpperCamelCase			  =      (np.arange(0   ,   lowerCamelCase_				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
    UpperCamelCase			  =      torch.from_numpy(lowerCamelCase_				).to(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						List[Any]=None   ,   lowerCamelCase_						:						Optional[int]=None   ,   lowerCamelCase_						:						int=None				):





    """simple docstring"""


    if prev_timestep is None:
     UpperCamelCase			  =      t - 1

    UpperCamelCase			  =      self.alphas_cumprod[t]
    UpperCamelCase			  =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    UpperCamelCase			  =      1 - alpha_prod_t
    UpperCamelCase			  =      1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
     UpperCamelCase			  =      self.betas[t]
    else:
     UpperCamelCase			  =      1 - alpha_prod_t / alpha_prod_t_prev

    # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
    # and sample from it to get previous sample
    # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
    UpperCamelCase			  =      beta_prod_t_prev / beta_prod_t * beta

    if variance_type is None:
     UpperCamelCase			  =      self.config.variance_type

    # hacks - were probably added for training stability
    if variance_type == "fixed_small_log":
     UpperCamelCase			  =      torch.log(torch.clamp(lowerCamelCase_   ,   min=1E-20				)				)
     UpperCamelCase			  =      torch.exp(0.5 * variance				)
    elif variance_type == "learned_range":
     # NOTE difference with DDPM scheduler
     UpperCamelCase			  =      variance.log()
     UpperCamelCase			  =      beta.log()

     UpperCamelCase			  =      (predicted_variance + 1) / 2
     UpperCamelCase			  =      frac * max_log + (1 - frac) * min_log

    return variance







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						Optional[int] = None   ,   lowerCamelCase_						:						Any=None   ,   lowerCamelCase_						:						bool = True   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      timestep

    if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
     UpperCamelCase  ,     UpperCamelCase			  =      torch.split(lowerCamelCase_   ,   sample.shape[1]   ,   dim=1				)
    else:
     UpperCamelCase			  =      None

    # 1. compute alphas, betas
    if prev_timestep is None:
     UpperCamelCase			  =      t - 1

    UpperCamelCase			  =      self.alphas_cumprod[t]
    UpperCamelCase			  =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
    UpperCamelCase			  =      1 - alpha_prod_t
    UpperCamelCase			  =      1 - alpha_prod_t_prev

    if prev_timestep == t - 1:
     UpperCamelCase			  =      self.betas[t]
     UpperCamelCase			  =      self.alphas[t]
    else:
     UpperCamelCase			  =      1 - alpha_prod_t / alpha_prod_t_prev
     UpperCamelCase			  =      1 - beta

    # 2. compute predicted original sample from predicted noise also called
    # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
    if self.config.prediction_type == "epsilon":
     UpperCamelCase			  =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
    elif self.config.prediction_type == "sample":
     UpperCamelCase			  =      model_output
    else:
     raise ValueError(
         f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
         """ for the UnCLIPScheduler."""				)

    # 3. Clip "predicted x_0"
    if self.config.clip_sample:
     UpperCamelCase			  =      torch.clamp(
         lowerCamelCase_   ,   -self.config.clip_sample_range   ,   self.config.clip_sample_range				)

    # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    UpperCamelCase			  =      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
    UpperCamelCase			  =      alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

    # 5. Compute predicted previous sample µ_t
    # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
    UpperCamelCase			  =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

    # 6. Add noise
    UpperCamelCase			  =      0
    if t > 0:
     UpperCamelCase			  =      randn_tensor(
         model_output.shape   ,   dtype=model_output.dtype   ,   generator=lowerCamelCase_   ,   device=model_output.device				)

     UpperCamelCase			  =      self._get_variance(
         lowerCamelCase_   ,   predicted_variance=lowerCamelCase_   ,   prev_timestep=lowerCamelCase_   ,   )

     if self.variance_type == "fixed_small_log":
      UpperCamelCase			  =      variance
     elif self.variance_type == "learned_range":
      UpperCamelCase			  =      (0.5 * variance).exp()
     else:
      raise ValueError(
          f"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
          """ for the UnCLIPScheduler."""				)

     UpperCamelCase			  =      variance * variance_noise

    UpperCamelCase			  =      pred_prev_sample + variance

    if not return_dict:
     return (pred_prev_sample,)

    return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_   ,   pred_original_sample=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						torch.FloatTensor   ,   lowerCamelCase_						:						torch.IntTensor   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.alphas_cumprod.to(device=original_samples.device   ,   dtype=original_samples.dtype				)
    UpperCamelCase			  =      timesteps.to(original_samples.device				)

    UpperCamelCase			  =      alphas_cumprod[timesteps] ** 0.5
    UpperCamelCase			  =      sqrt_alpha_prod.flatten()
    while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
     UpperCamelCase			  =      sqrt_alpha_prod.unsqueeze(-1				)

    UpperCamelCase			  =      (1 - alphas_cumprod[timesteps]) ** 0.5
    UpperCamelCase			  =      sqrt_one_minus_alpha_prod.flatten()
    while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
     UpperCamelCase			  =      sqrt_one_minus_alpha_prod.unsqueeze(-1				)

    UpperCamelCase			  =      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples

import datasets

from .nmt_bleu import compute_bleu  # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py


_SCREAMING_SNAKE_CASE 		=      """\
@INPROCEEDINGS{Papineni02bleu:a,
    author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},
    title = {BLEU: a Method for Automatic Evaluation of Machine Translation},
    booktitle = {},
    year = {2002},
    pages = {311--318}
}
@inproceedings{lin-och-2004-orange,
    title = \"{ORANGE}: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation\",
    author = \"Lin, Chin-Yew  and
      Och, Franz Josef\",
    booktitle = \"{COLING} 2004: Proceedings of the 20th International Conference on Computational Linguistics\",
    month = \"aug 23{--}aug 27\",
    year = \"2004\",
    address = \"Geneva, Switzerland\",
    publisher = \"COLING\",
    url = \"https://www.aclweb.org/anthology/C04-1072\",
    pages = \"501--507\",
}
"""

_SCREAMING_SNAKE_CASE 		=      """\
BLEU (bilingual evaluation understudy) is an algorithm for evaluating the quality of text which has been machine-translated from one natural language to another.
Quality is considered to be the correspondence between a machine's output and that of a human: \"the closer a machine translation is to a professional human translation,
the better it is\" – this is the central idea behind BLEU. BLEU was one of the first metrics to claim a high correlation with human judgements of quality, and
remains one of the most popular automated and inexpensive metrics.

Scores are calculated for individual translated segments—generally sentences—by comparing them with a set of good quality reference translations.
Those scores are then averaged over the whole corpus to reach an estimate of the translation's overall quality. Intelligibility or grammatical correctness
are not taken into account[citation needed].

BLEU's output is always a number between 0 and 1. This value indicates how similar the candidate text is to the reference texts, with values closer to 1
representing more similar texts. Few human translations will attain a score of 1, since this would indicate that the candidate is identical to one of the
reference translations. For this reason, it is not necessary to attain a score of 1. Because there are more opportunities to match, adding additional
reference translations will increase the BLEU score.
"""

_SCREAMING_SNAKE_CASE 		=      """
Computes BLEU score of translated segments against one or more references.
Args:
    predictions: list of translations to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
    max_order: Maximum n-gram order to use when computing BLEU score.
    smooth: Whether or not to apply Lin et al. 2004 smoothing.
Returns:
    'bleu': bleu score,
    'precisions': geometric mean of n-gram precisions,
    'brevity_penalty': brevity penalty,
    'length_ratio': ratio of lengths,
    'translation_length': translation_length,
    'reference_length': reference_length
Examples:

    >>> predictions = [
    ...     [\"hello\", \"there\", \"general\", \"kenobi\"],                             # tokenized prediction of the first sample
    ...     [\"foo\", \"bar\", \"foobar\"]                                             # tokenized prediction of the second sample
    ... ]
    >>> references = [
    ...     [[\"hello\", \"there\", \"general\", \"kenobi\"], [\"hello\", \"there\", \"!\"]],  # tokenized references for the first sample (2 references)
    ...     [[\"foo\", \"bar\", \"foobar\"]]                                           # tokenized references for the second sample (1 reference)
    ... ]
    >>> bleu = datasets.load_metric(\"bleu\")
    >>> results = bleu.compute(predictions=predictions, references=references)
    >>> print(results[\"bleu\"])
    1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,     _KWARGS_DESCRIPTION		)
class SCREAMING_SNAKE_CASE_		(      datasets.Metric		):







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return datasets.MetricInfo(
        description=_DESCRIPTION   ,   citation=_CITATION   ,   inputs_description=_KWARGS_DESCRIPTION   ,   features=datasets.Features(
            {
                """predictions""": datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				),
                """references""": datasets.Sequence(
                    datasets.Sequence(datasets.Value("""string"""   ,   id="""token"""				)   ,   id="""sequence"""				)   ,   id="""references"""				),
            }				)   ,   codebase_urls=["""https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py"""]   ,   reference_urls=[
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						List[Any]   ,   lowerCamelCase_						:						List[str]   ,   lowerCamelCase_						:						Optional[Any]=4   ,   lowerCamelCase_						:						List[Any]=False				):





    """simple docstring"""


    UpperCamelCase			  =      compute_bleu(
        reference_corpus=lowerCamelCase_   ,   translation_corpus=lowerCamelCase_   ,   max_order=lowerCamelCase_   ,   smooth=lowerCamelCase_				)
    ((UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase)  ,     (UpperCamelCase))			  =      score
    return {
        "bleu": bleu,
        "precisions": precisions,
        "brevity_penalty": bp,
        "length_ratio": ratio,
        "translation_length": translation_length,
        "reference_length": reference_length,
    }
